AI to Accelerate Race to Build Smarter Cities

Building so-called Smarter Cities has long been touted as a major goal for municipalities around the globe. But building systems capable of responding to events in real time is a major challenge for any government operating on a limited IT budget. The hope is that processes will become integrated enough to create a massive pool of data that will then be employed to drive any number of artificial intelligence (AI) applications.

The problem is that while city governments typically have access to massive amounts of data, most of it resides in isolated systems run by departments that are often at odds with one another. In fact, Daniel Newman, principal analyst with Futurum, says smart cities are mostly a figment of vendor marketing imagination.

“The trouble with smart cities is they don’t exist yet,” says Newman.

What is being built is a set of isolated next-generation applications working with a very limited set of data. But while isolated applications offer some compelling returns on investment (ROI), many local governments have yet to fully appreciate the business and cultural impact so-called Big Data will play in driving AI applications that will transform almost every government process.

Notable exceptions, however, include Indiana and Illinois. Back in 2014, then Governor Mike Pence of Indiana issued an executive order to create a massive data hub. Illinois has a similar data initiative. These types of initiatives long term will be critical because machine and deep learning algorithms that drive AI models are only as good as the amount of data they can be applied against. Before localities can take advantage of those algorithms, they will need to create massive Big Data hubs.

In the meantime, the three things that most drive governments to invest in smart city projects are a need to be economically competitive, sustainability and public safety, says Dante Ricci, global lead public services and health care marketing and communications for SAP.

Attracting businesses to a locality is critical to growing the tax base. It’s already clear a race is on to put the types of advanced IT systems in place that will attract business investment. Amazon, for example, in its search for a new headquarters somewhere on the east coast of the U.S., has made it clear that investments in IT infrastructure are just as important as transit and other forms of physical infrastructure. Just about every company now takes note of those same requirements before deciding to build another factory or open a new office because they impact quality of life, which in turn impacts the number and types of employees an organization can hire.

The challenge most local governments face is that they don’t have ready access to funding to create even a proof of concept (PoC) project, says Ricci. Many of them rely on various non-profit organizations and government think tanks to create those projects, says Ricci.

“It’s a way to see what the art of the possible is,” says Ricci.

It’s worth noting that cities located in regions run by totalitarian governments may, for better or worse, have a distinct advantage when it comes to aggregating data as part of a series of smart city projects. One locality that is well down the path toward building a smart city, for example, is Singapore, which last year was recognized by Juniper Research as the “cleverest” city on earth, followed by London, New York and San Francisco.

Each of these cities has access to substantial economic resources. But over the long haul, it’s probable that cities such as Singapore located in countries that are not liberal democracies might have a significant short-term advantage, notes Larry Carvalho, an industry analyst with International Data Corp. (IDC). A big part of the reason for that is that a totalitarian government can mandate change.

“You can get there faster when you control everything,” says Carvalho. “Liberal democracies will get there. It will just take longer because of privacy concerns.”

For example, Cape Town in South Africa has worked with SAP to deploy a centralized Emergency Policing and Incident Command (EPIC) to coordinate the efforts of fire and rescue, traffic, metro police, law enforcement, disaster risk management, and the city’s special investigative unit.

At a simpler level, Big Data analytics coupled with AI should also be able to make suggestions about when to optimally issue work permits spanning multiple agencies that would substantially reduce traffic jams. There might even come a day when traffic itself is managed in real time using AI applications infused with machine and deep learning algorithms.

Obviously, it’s still early days when it comes to building smart cities. No one should expect cities to magically transform overnight. It may take years for the technologies such as machine and deep learning that would enable a smart city to be fully formed and mature to the point where they can be widely implemented. There’s already a significant shortage of AI and data science talent. Most local governments are not going to be able to hire and retain that talent without either financial help from a central government or a grant from an IT vendor anxious to develop a broader market opportunity.

The one thing that is clear is that AI advances in one form or another are coming to cities, like it or not. It’s also probable that some of those advances are going to be perceived to be controversial. But as is often the case, most people are willing, at least to a point, to make some philosophical adjustments in the interests of the greater good.

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